1. Field
The present invention relates generally to communication, and more specifically to techniques for scaling and quantizing soft-decision metrics for decoding in a wireless communication system.
2. Background
In a wireless communication system, a transmitter encodes and interleaves traffic data, typically one data packet at a time, to obtain interleaved data. The transmitter may further partition each packet of interleaved data into multiple output blocks for transmission at different times. The transmitter then modulates and transmits these output blocks over a wireless channel at the designated times. If the transmission for these output blocks is not continuous, then the transmission appears as “bursts”, one burst for each output block. The wireless channel distorts each transmitted burst with a particular channel response and further degrades each transmitted burst with noise and interference.
A receiver receives the transmitted bursts and processes each received burst to obtain soft-decision metrics (or simply, “soft metrics”) for the burst. A soft metric is a multi-bit value obtained by the receiver for a single-bit (or “hard”) value sent by the transmitter. In one conventional method, the receiver scales the soft metrics for all received bursts for a given data packet with a single scaling factor to obtain scaled soft metrics for these bursts. The scaling factor is selected such that soft metrics with the proper amplitude are provided for subsequent processing. Typically, the scaling factor is derived based on a signal-to-noise-and-interference ratio (SNR) estimate for the bursts. The scaled soft metrics are then deinterleaved and decoded to obtain decoded data for the packet.
Scaling all of the received bursts for a data packet with a single scaling factor effectively gives these bursts equal weight in the decoding process. This is acceptable if the wireless channel is relatively static and the bursts are received with similar signal quality. However, if the bursts are transmitted at different times, then these bursts may experience different channel conditions and achieve different SNRs. In this case, scaling all bursts for a data packet with a single scaling factor results in sub-optimal decoding performance.
In another conventional method, the scaling is performed based on an average SNR obtained for multiple prior bursts. For an additive white Gaussian noise (AWGN) wireless channel with flat fading, the SNR may not change much from burst to burst, and good performance may be achieved by scaling the current burst based on the average SNR for the prior bursts. However, for a fading and interference wireless channel, the channel conditions are not static, the statistics of the soft metrics can change from burst to burst, and the SNR for the current burst may not correlate well with the average SNR for the prior bursts. Furthermore, the average SNR is not available for the first burst of a transmission or may be quite unreliable after a long period of no transmission, such as for a discontinuous transmission (DTX). Thus, decoding performance may be poor under uncertain conditions for this method.
There is therefore a need in the art for techniques to more properly scale soft metrics for bursts transmitted at different times and possibly having different statistics and signal quality.